Steffen Ventz's repositories
Understanding-Deep-Learning-book
Understanding Deep Learning - Simon J.D. Prince
logisticbeta-reproduce
Reproducing codes of: Logistic-beta processes for modeling dependent random probabilities with beta marginals
bayesianIsotonicSims
Code to accompany Boonstra, Owen, and Kang (2023)
PS_survival
Simulation codes for Principal Stratification Analysis of Noncompliance with Time-to-Event Outcomes
vistan
A simple library to run variational inference on Stan models.
rapidMTM
Rapidly mixing multiple-try Metropolis algorithms for model selection problems
SLIMTutorials
Tutorials for the 2021 ML4Seismic Partners Meeting
pytorch-deep-learning
Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.
SBFA
Integrative Analysis of Multi-omics and Imaging Data with Incorporation of Biological Information via Structural Bayesian Factor Analysis
AdaFNN
[ICML 2021] Deep Learning for Functional Data Analysis with Adaptive Basis Layers
conformal-prediction
Lightweight, useful implementation of conformal prediction on real data.
ltt
Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control
FundamentalsNumericalComputation.jl
Core functions for the Julia (2nd) edition of the text Fundamentals of Numerical Computation, by Driscoll and Braun.
2022-ISBA-BNPCI
Github repo for the BNP Causal Inference short course
ZZpaper
append code to Corbella 2022
gpbart
Implementation of GP-BART algorithm.
KSD-Bayes
Experiment Code for "Robust Generalised Bayesian Inference for Intractable Likelihoods" (https://arxiv.org/abs/2104.07359)
causal_inference_julia_code
Julia code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins
conformal_classification
Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true class with high probability (via conformal prediction).
intro_bayesian_causal
Repository for Introduction to Bayesian Estimation of Causal Effects
VAE_Julia
Julia implementation of Variational Auto Encoder (VAE) model